Load-Aware Dynamic Access for Ultra-Dense Small Cell Networks: A Hypergraph Game Theoretic Solution

In this paper we research the load-aware channel allocation in ultra-dense small cell networks based on the hypergraph interference model. Cumulative interference is a hard nut to crack in ultra-dense networks because of the intensive distribution of low-powered and small-coverage small cells. The traditional binary graph interference model, which mainly focused on the pair-wise strong interference relation, can not capture the cumulative interference. Therefore, we use the hypergraph model to accurately describe the complex interference relation among small cells. The applications of hypergraph in wireless networks is in its infant stage. Considering the practical traffic demands of small cells, they can access multiple channels. To cope with this problem, we formulate the multi-channel access problem as a local altruistic hypergraph game and prove that it is an exact potential game, which admits at least one pure strategy Nash Equilibrium. To overcome the complexity of the centralized method and the constraint on the direct information exchange among small cells in hyperedges, a cloud-based centralized-distributed model is utilized. With the information shared in the cloud, a centralized-distributed learning algorithm can quickly search the Nash Equilibrium. The simulation results show that the proposed algorithm is superior to the existing binary graph-based schemes and significantly improves the communication efficiency.

[1]  Akihiko Matsui,et al.  Best response dynamics and socially stable strategies , 1992 .

[2]  Alagan Anpalagan,et al.  Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games , 2012, IEEE Journal of Selected Topics in Signal Processing.

[3]  Qihui Wu,et al.  Distributed Channel Access for Device-to-Device Communications: A Hypergraph-Based Learning Solution , 2017, IEEE Communications Letters.

[4]  Meixia Tao,et al.  Hypergraph-based frequency reuse in dense femtocell networks , 2013, 2013 IEEE/CIC International Conference on Communications in China (ICCC).

[5]  Lazaros F. Merakos,et al.  A graph-coloring secondary resource allocation for D2D communications in LTE networks , 2012, 2012 IEEE 17th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[6]  Masakazu Sengoku,et al.  Graph theoretic or computational geometric research of cellular mobile communications , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[7]  Alain Bretto,et al.  Hypergraph Theory: An Introduction , 2013 .

[8]  Yuan He,et al.  Hypergraph-Based Intercell Interference Coordination for QoS Guarantees in Dense Femtocell Networks , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[9]  Qiao Li,et al.  Maximal Scheduling in a Hypergraph Model for Wireless Networks , 2008, 2008 IEEE International Conference on Communications.

[10]  Alagan Anpalagan,et al.  Load-Aware Dynamic Spectrum Access for Small-Cell Networks: A Graphical Game Approach , 2015, IEEE Transactions on Vehicular Technology.

[11]  Xiang Cheng,et al.  Interference-aware graph based resource sharing for device-to-device communications underlaying cellular networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  L. Shapley,et al.  REGULAR ARTICLEPotential Games , 1996 .

[13]  Zhu Han,et al.  Radio Resource Allocation for Device-to-Device Underlay Communication Using Hypergraph Theory , 2016, IEEE Transactions on Wireless Communications.